Executive functions in Thai adolescents: Development of an inventory measure, its factors, and norms
Bibliographic record
Abstract
OBJECTIVE: Thai researchers developed a new self-report measure of executive functions for adolescents based on Diamond's framework (the Behavioral Inventory Measure of Executive Functions [BIMEFs]). How it was developed, its psychometric properties, and norms by sex and age are reported here. METHOD: An independent panel of experts evaluated the content validity of BIMEFs. Reliability was checked using Cronbach's alpha with a sample of 45 secondary students. 1,865 students, ages 12 - 18 years (65% female) from across Thailand participated in the normative study. RESULTS: The BIMEFs consists of 42 items that assess inhibitory control (IC), working memory (WM), and cognitive flexibility (CF), including eight subcomponents. For all items, the index of item-objective congruence was >0.5 and Cronbach's alpha was >0.7. Confirmatory factor analysis (CFA) showed the adjusted goodness of fit index to be 0.9. The strongest sex difference was for IC. Students of 13 years scored lower on EFs overall, IC, WM, CF, and all subcomponents than older students. Self-control, verbal working memory, and being able to change perspectives showed the most pronounced differences by age. CONCLUSION: The BIMEFs, which is designed to be culturally-appropriate for Thailand and cross-culturally generally, is the first EF questionnaire based on Diamond's framework. It shows good psychometric properties and sensitivity to age and sex differences. It indicates that IC development, at least in Thailand, plateaus earlier than WM and CF and that CF shows a more protracted development during adolescence than IC or WM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".